Method to predict min cell voltage from discrete min cell voltage output of stack health monitor

ABSTRACT

A system for estimating parameters of a fuel cell stack. The system includes a stack health monitor for monitoring minimum cell voltage, stack voltage and current density of the fuel cell stack. The stack health monitor also indicates when a predetermined minimum cell voltage threshold level has been achieved. The system further includes a controller configured to control the fuel cell stack, where the controller determines and records the average fuel cell voltage. The controller generates and stores artificial data points proximate to the one or more predetermined minimum cell voltage threshold levels each time the minimum cell voltage drops below the one or more predetermined minimum cell voltage threshold levels so as to provide an estimation of the fuel cell stack parameters including a minimum cell voltage trend and a minimum cell voltage polarization curve.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to a system and method for predictingthe minimum cell voltage trend of fuel cells in a fuel cell stack and,more particularly, to a system and method for predicting the minimumcell voltage trend of fuel cells in a fuel cell stack using a discreteminimum cell voltage output from a stack health monitor.

2. Discussion of the Related Art

Hydrogen is a very attractive fuel because it is clean and can be usedto efficiently produce electricity in a fuel cell. A hydrogen fuel cellis an electro-chemical device that includes an anode and a cathode withan electrolyte therebetween. The anode receives hydrogen gas and thecathode receives oxygen or air. The hydrogen gas is dissociated in theanode to generate free hydrogen protons and electrons. The hydrogenprotons pass through the electrolyte to the cathode. The hydrogenprotons react with the oxygen and the electrons in the cathode togenerate water. The electrons from the anode cannot pass through theelectrolyte, and thus are directed through a load to perform work beforebeing sent to the cathode.

Proton exchange membrane fuel cells (PEMFC) are a popular fuel cell forvehicles. The PEMFC generally includes a solid polymer electrolyteproton conducting membrane, such as a perfluorosulfonic acid membrane.The anode and cathode typically include finely divided catalyticparticles, usually platinum (Pt), supported on carbon particles andmixed with an ionomer. The catalytic mixture is deposited on opposingsides of the membrane. The combination of the anode catalytic mixture,the cathode catalytic mixture and the membrane define a membraneelectrode assembly (MEA). MEAs are relatively expensive to manufactureand require certain conditions for effective operation.

Several fuel cells are typically combined in a fuel cell stack togenerate the desired power. The fuel cell stack receives a cathode inputgas, typically a flow of air forced through the stack by a compressor.Not all of the oxygen is consumed by the stack and some of the air isoutput as a cathode exhaust gas that may include water as a stackby-product. The fuel cell stack also receives an anode hydrogen inputgas that flows into the anode side of the stack.

The stack controller needs to know the current/voltage relationship,referred to as a polarization curve, of the fuel cell stack to provide aproper distribution of power from the stack. The relationship betweenthe voltage and the current of the stack is typically difficult todefine because it is non-linear, and changes depending on manyvariables, including stack temperature, stack partial pressures andcathode and anode stoichiometries. Additionally, the relationshipbetween the stack current and voltage changes as the stack degrades overtime. Particularly, an older stack will have lower cell voltages, andwill need to provide more current to meet the power demands than a new,non-degraded stack. Fortunately, many fuel cell systems, once they areabove a certain temperature, tend to have repeatable operatingconditions at a given current density. In those instances, the voltagecan be approximately described as a function of stack current densityand age.

The minimum cell voltage of the fuel cells in a fuel cell stack is avery important parameter for monitoring the stack health and protectingthe stack from reverse voltage damage. In addition, the minimum cellvoltage is used for many purposes for controlling the fuel cell stack,such as power limitation algorithms, anode nitrogen bleeding, diagnosticfunctions, etc. However, the cost of known cell voltage monitors thatemploy a continuous minimum cell voltage output and have a 0.5 mVresolution is extremely high. Therefore, there is a need in the art fordetermining the minimum cell voltage of the fuel cells in a fuel cellstack without requiring the use of costly monitoring components,including the cost associated with recording and storing informationprovided by the monitoring components at each time step.

SUMMARY OF THE INVENTION

In accordance with the teachings of the present invention, a system forestimating parameters of a fuel cell stack is disclosed. The systemincludes a stack health monitor for monitoring minimum cell voltage,stack voltage and current density of the fuel cell stack. The stackhealth monitor also indicates when a predetermined minimum cell voltagethreshold level has been achieved. The system further includes acontroller configured to control the fuel cell stack, where thecontroller determines and records the average fuel cell voltage. Thecontroller generates and stores artificial data points proximate to theone or more predetermined minimum cell voltage threshold levels eachtime the minimum cell voltage drops below the one or more predeterminedminimum cell voltage threshold levels so as to provide an estimation ofthe fuel cell stack parameters including a minimum cell voltage trendand a minimum cell voltage polarization curve.

Additional features of the present invention will become apparent fromthe following description and appended claims, taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph with stack current density on the horizontal axis andstack voltage on the vertical axis showing a fuel cell stackpolarization curve for a new stack and an older stack;

FIG. 2 is a block diagram of a fuel cell system including a fuel cellstack, a stack health monitor and a controller;

FIG. 3 is a flow chart diagram showing a process for an algorithm thatestimates a polarization curve for a fuel cell stack online;

FIG. 4 is a flow chart diagram showing a process for an algorithm thatestimates a polarization curve for a fuel cell stack; and

FIG. 5 is a graph with stack current density on the horizontal axis andcell voltage on the vertical axis showing the accuracy of a predictedminimum cell voltage trend.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following discussion of the embodiments of the invention directed toa system and method for predicting the minimum cell voltage trend offuel cells in a fuel cell stack is merely exemplary in nature, and is inno way intended to limit the invention or its applications or uses.

Many control parameters of a fuel cell system require knowledge of thepolarization curve of the fuel cell stack, such as knowing the maximumvoltage potential and current draw available from the fuel cell stack.As the stack ages, the stack polarization curve also changes as a resultof stack degradation. FIG. 1 is a graph with stack current density onthe horizontal axis and average cell voltage on the vertical axis. Graphline 10 is a polarization curve for a new fuel cell stack and graph line12 is a polarization curve for an aged fuel cell stack, where theaverage cell voltage is reduced for the same stack current density forthe older stack. Therefore, it is necessary to update the polarizationcurve for the stack so as to accurately determine the various controlparameters for efficient fuel cell stack operation.

FIG. 2 is a block diagram of a fuel cell system 18 that includes a fuelcell stack 20, a stack health monitor 22 and a controller 24. Thecontroller 24 receives data from the stack health monitor 22 and usesthe data collected from the stack health monitor 22, such as minimumcell voltage, average cell voltage and current density, and whether oneor more cell voltage trigger levels have been achieved, to calculate theminimum cell voltage polarization curve of the stack 20 after at leastone minimum cell voltage threshold level trigger has been achieved, asdescribed in detail below. In an alternate embodiment, a separateminimum cell voltage monitoring device may be used to measure minimumcell voltage, while the stack health monitor 22 collects data regardingaverage cell voltage and current density.

A polarization curve based on the average cell voltage and the stackcurrent density, and a polarization curve based on the minimum cellvoltage and stack current density are typically estimatedsimultaneously. FIG. 3 is a flow chart diagram 30 showing the high-leveloperation of an algorithm for calculating the polarization curve of thefuel cell stack by the controller 24. At box 32, the algorithm waits forthe fuel cell stack 20 to operate and provide power. When the fuel cellstack 20 provides power, the algorithm will not record voltage data forminimum cell voltage at box 34 until a trigger level of minimum cellvoltage has been achieved, although minimum cell voltage is continuouslymonitored, as is discussed in detail below.

In a previously disclosed algorithm for calculating a minimum cellvoltage polarization curve, disclosed in U.S. patent application Ser.No. 11/669,898, entitled, “Algorithm for Online Adaptive PolarizationCurve Estimation of a Fuel Cell Stack,” assigned to the assignee of thepresent application and incorporated herein by reference, the algorithmutilizes all of the continuous stack current density data and stackvoltage data to calculate an average cell voltage and a minimum cellvoltage, or may monitor the cell voltage of each fuel cell in the stackto determine average cell voltage and minimum cell voltage. Thus, all ofthe continuous data is utilized and stored in a memory of a controller,such as the controller 24, which can be costly due to the amount of datathat is recorded and stored, as well as the cost associated with thecomponents required to continuously perform these measurements. Inaddition, dynamic information about average cell voltage and acorresponding polarization curve is not known because the polarizationcurves are determined after the fuel cell stack has been shutdown.

The algorithm discussed below for determining a minimum cell voltagetrend of the fuel cell stack 20 does not record and store all of thecontinuous minimum cell voltage data inputted before the fuel cell stack20 is shutdown. Instead, the algorithm utilizes information from thestack health monitor 22, such as minimum cell voltage, average fuel cellvoltage and current density, to estimate stack parameters after one ormore trigger levels of minimum cell voltage have been achieved, asdetermined by the stack health monitor. In this way, data regardingminimum cell voltage does not need to be recorded and stored at everytime step, thereby reducing the cost associated with recording andstoring the data regarding minimum cell voltage. In addition, thealgorithm is dynamic, thus reflecting immediate changes in cell voltageand corresponding current density once the trigger level or levels havebeen achieved. Providing more trigger levels will enable the algorithmto better predict whether or not the fuel cell stack 20 will recovervoltage or will continue to drop in voltage, as is discussed in moredetail below.

FIG. 4 is a flow chart diagram 40 for the algorithm for estimating stackparameters at the box 34 based on average cell voltages andcorresponding current density that utilizes two trigger levels ofminimum cell voltage, as described in more detail below. As the stackhealth monitor 22 outputs the information regarding minimum cellvoltage, average cell voltage and corresponding current density, andmultiple (one or more) discrete levels of the minimum cell voltage for agiven current density, the algorithm uses the multiple discrete levelsof minimum cell voltage output, in addition to continuous average cellvoltage data to predict the minimum cell voltage trend and the minimumcell voltage polarization curve. Continuous average cell voltage andcurrent density data may be provided by the stack health monitor 22, aboost converter, or various other cell voltage measurement devices.

As discussed above, the minimum cell voltage is not recorded at everytime-step. Instead, the minimum cell voltage and corresponding currentdensity will only be recorded if one or more predetermined thresholdtrigger level(s) are achieved. Average cell voltage and current densitydata are collected by the controller 24 at box 44 and saved into databins that are divided by current density values, and this begins oncethe fuel cell system 18 is in a run state. Next, the algorithmdetermines if a first threshold level trigger for minimum cell voltagehas been achieved at decision diamond 46, indicating that a fuel cell inthe stack has achieved the threshold level.

For example, the minimum cell voltage may be triggered if one of thecells in the stack has a voltage of 0.45 volts and the correspondingcurrent density is 0.8 A/cm². If yes, the stack health monitor indicatesthat a trigger has been hit and the stored average cell voltage data isoverwritten in nearby bins at box 48. The data filled into the data binsfor parameter estimation may be, for example, 0.45 volts as the minimumcell voltage for the current density range of 0.8±c where c can be atuned parameter, as can be seen in FIG. 5, discussed below.

Overwriting data in the nearby bins at the box 48 helps to represent theminimum cell voltage at the predetermined trigger level, therebyenabling the algorithm to more accurately predict the parameterestimation, i.e., the minimum cell voltage trend and the minimum cellvoltage polarization curve, which enables the controller 24 to set adesirable current set-point value for the fuel cell stack 20.

Once the first trigger level is achieved at the decision diamond 46, andthe data is overwritten in the nearby bins at the box 48, the algorithmdetermines if a second threshold level trigger for a minimum cellvoltage has been achieved at decision diamond 50. For example, theminimum cell voltage trigger for the second trigger level may be 0.30volts and the corresponding current density may be 1.0 A/cm². If yes,the stack health monitor indicates that the second trigger has been hitand the stored average cell voltage data is overwritten in nearby binsat box 52, where the data filled into the data bins may be, for example,0.30 volts as the minimum cell voltage for the current density range of1.0±c, where c can be a tuned parameter, as shown in FIG. 5 anddiscussed below. If the second trigger level has not been achieved, thealgorithm continues to collect average cell voltage and correspondingcurrent density data at the box 44 and does not estimate parametersaccording to one embodiment. In an alternate embodiment, the parameterestimation may begin after the first trigger level is achieved,regardless of whether the second trigger level is achieved, as discussedin more detail below.

Once the first and second trigger levels have been achieved at thedecision diamonds 46 and 50, respectively, and the data has beenoverwritten in the nearby bins at the boxes 48 and 52, respectively, thealgorithm will verify that the data collected are sufficient to estimatethe parameters of the stack 20. If the data are sufficient, thealgorithm estimates the fuel cell system parameters, including theminimum cell voltage trend and a minimum cell voltage polarizationcurve, as shown in box 36 of FIG. 3. The parameter estimation at the box36 utilizes the same equation for determining the minimum cell voltagepolarization curve as the previously disclosed algorithm discussedabove, however, the data utilized in the equation is different. Inparticular, the data overwritten in the nearby bins at the boxes 48 and52 creates more data points near the first and second trigger levels,thus providing a more robust estimation of the polarization curve forthe minimum cell voltage without requiring minimum cell voltage andcurrent density data to be collected at every time step. A polarizationcurve for the minimum cell voltage is assumed to exist based onexperimental data that shows a correlation between minimum cell voltageand change in current density.

FIG. 5 is a graph with stack current density on the horizontal axis andcell voltage on the vertical axis showing the artificial data pointscreated according to the algorithm of the present invention. A clusterof data points 70 represents the artificially created data points thatare overwritten in the nearby bins at the box 48, and a cluster of datapoints 72 represents the artificially created data points that areoverwritten in the nearby bins at the box 52. Line 76 illustrates theestimated parameters according to the previously disclosed algorithm,discussed above, line 78 illustrates the estimated parameters accordingto the algorithm of the present invention, and line 80 illustratesexperimental data. FIG. 5 shows that the algorithm of the inventionaccurately estimates the drop in cell voltage for the correspondingcurrent density, that is, accurately estimates the trend in minimum cellvoltage.

As discussed above, the parameters of the fuel cell system 18 may beestimated after the first trigger level has been achieved at thedecision diamond 50, even if the second trigger level has not beenachieved, if the data collected are sufficient by using the dataoverwritten in the nearby bins at the box 48 and then continuouslycollecting data regarding cell voltage and current density to capturedynamic changes that may affect the estimated parameters. To estimatestack parameters after the first trigger level has been achieved, theamount of data gathered must be sufficient, as determined by the currentdensity range of the stack 20. Alternatively, the parameters of the fuelcell system 18 may be estimated after more than two trigger levels havebeen achieved, although not shown in FIG. 4 for the sake of clarity, andthen continuously collecting data regarding average cell voltage andcurrent density to capture dynamic changes that may affect the estimatedparameters. More than two trigger levels typically implies that the datagathered is sufficient, as it is expected that the trigger levels willhave varying current densities. Generally, the more trigger levels thatare used, the more accurate the parameter estimation will be.

Once the parameter estimation is complete at the box 36, the parameterestimation is stored in non-volatile memory of the controller 24 at box38. In addition, once the parameter estimation starts at the box 36, thealgorithm will continuously predict the parameters based on the dynamicdata input, as discussed above, including a minimum cell voltage trendand a minimum cell voltage polarization curve.

After the parameter estimation is complete at the box 36, the algorithmproceeds to box 38 to store the estimated parameters that are used todetermine the polarization curves in non-volatile memory. In onenon-limiting embodiment, a predetermined cell voltage model is used todetermine the parameters as:

$\begin{matrix}{E_{cell} = {E_{rev} - {\left( {i + a} \right)*R_{HFR}} - \left( {{0.07*{\log_{10}\left( \frac{i + a}{i^{0}} \right)}} + {c\;{\log_{10}\left( {1 - \frac{i}{i^{\infty}}} \right)}}} \right)}} & (1)\end{matrix}$Where the Following Measurements are Provided:

-   E_(cell)=Cell voltage (V)-   i=Current density (A/cm²); and-   R_(HFR)=Cell HFR resistance measurement or from model (ohm cm²).    The Following Assumed Constants are Provided:-   E_(rev)=Thermodynamic reversible cell potential (V); and-   a=Background current density from Cell Shorting/Cell crossover    (A/cm²).    The Following Parameters are Provided:-   i⁰=Exchange current density (A/cm²);-   i^(∞)=Limiting current density (A/cm²); and-   c=Mass transfer coefficient.

For a system with very repeatable membrane humidification control,R_(HFR) might be represented as a function of current density.Similarly, E_(rev) might also be represented as a function of currentdensity. This suggests that at each current density, the operatingpressure, temperature, stoichiometry and humidification are sufficientlyrepeatable to use a simplistic term. In another embodiment, the averageR_(HFR) could be either measured or calculated at each count, andaveraged. The value E_(rev) could be computed the same way, based ontemperature and pressure data at each count.

The cell voltage model of equation (1) can be simplified by ignoring theconstant a so that equation (1) becomes:

$\begin{matrix}{E_{cell} = {E_{rev} - {(i)*R_{HFR}} - \left( {{0.07*{\log_{10}\left( \frac{i}{i^{0}} \right)}} + {c\;{\log_{10}\left( {1 - \frac{i}{i^{\infty}}} \right)}}} \right)}} & (2)\end{matrix}$

Rearranging the terms in equation (2) gives:

$\begin{matrix}{{E_{rev} - {(i)*R_{HFR}} - E_{cell}} = \left( {{0.07*{\log_{10}\left( \frac{i}{i^{0}} \right)}} + {c\;{\log_{10}\left( {1 - \frac{i}{i^{\infty}}} \right)}}} \right)} & (3)\end{matrix}$

To provide the parameter estimation, the following variables aredefined:y=E _(rev)−(i)*R _(HFR) −E _(cell)x=iθ₁=i⁰θ₂=i^(∞)θ₃=c

Equation (3) can be represented in the following form:y=F(x,θ)  (4)

Thus, equation (3) can be represented as:

$\begin{matrix}{y = \left( {{0.07*{\log_{10}\left( \frac{x}{\theta_{1}} \right)}} + {\theta_{3}\;{\log_{10}\left( {1 - \frac{x}{\theta_{2}}} \right)}}} \right)} & (5)\end{matrix}$In equation (5), the input-output pair is (x,y) and the parameters to beestimated are θ=[θ₁,θ₂,θ₃]^(T).

For a given training set G=x(i),y(i):(i=1,2, . . . , M), a cost functionto be minimized can be defined as:

$\begin{matrix}{{J\left( {\theta,G} \right)} = {\frac{1}{2}{\sum\limits_{i = 1}^{M}{{{y(i)} - {F\left( {{x(i)},\theta} \right)}}}^{2}}}} & (6)\end{matrix}$

By letting ε(i)=y(i)−F)(x(i),θ), equation (6) becomes:

$\begin{matrix}{{J\left( {\theta,G} \right)} = {{\frac{1}{2}{\sum\limits_{i = 1}^{M}{{ɛ(i)}^{T}{ɛ(i)}}}} = {\frac{1}{2}{ɛ\left( {\theta,G} \right)}^{T}{ɛ\left( {\theta,G} \right)}}}} & (7)\end{matrix}$Therefore, the parameter estimation solves a non-linear least squaresproblem so that the solution θ=[θ₁,θ₂,θ₃]^(T) minimizes J(θ,G).

The non-linear least squares problem can be solved using any suitablenumerical method, such as a Gauss-Newton estimation with aLevenberg-Marquardt update method. The Gauss-Newton approach can besummarized by linearizing an error ε(θ,G) at the current value of θ(k),where k is an iteration index, and solving the least squares problem tominimize the error value and estimate θ(k+1). In one embodiment, thecomputation is minimized by setting the value θ₂ to a constant θ_(c) andestimating the other two parameters θ₁ and θ₃. However, this is by wayof a non-limiting example in that all three of the parameters θ₁, θ₂ andθ₃ can be estimated by the algorithm or any other suitable parameters.

In other embodiments, different techniques could be used to solveequation (7). For example, for stacks in which performance isinsensitive to the parameter i^(∞), this parameter could be replacedwith a constant. Then the parameters i⁰ and c could be solvedsequentially. The parameter i⁰ could be solved by using data collectedat low enough current density to minimize mass transport losses, buthigh enough to minimize the effect of permeation (0.05-0.1 A/cm²). Thenthe resulting equation could be solved with the high current densitydata to obtain the parameter c.

The algorithm may also determine whether the estimated parametersprovide or exceed a predetermined estimation success criteria (ESC).Particularly, in one non-limiting embodiment, the calculated parametersmust satisfy the equation:(θ(k+1)−θ(k))^(T)(θ(k+1)−θ(k))≦ωθ(k)^(T)θ(k)  (8)Where ω is a tunable parameter used to determine the steady state of theestimation.

Once the estimation of the parameters is complete at the box 36 as shownin FIG. 3, the algorithm stores the estimated parameters in anon-volatile random access memory (NV RAM) at the box 38, as discussedabove. The controller 24 can then access the NV RAM to get the currentestimation parameters, which can then be used to calculate thepolarization curve in a manner that is well understood by those skilledin the art. Once the algorithm starts to estimate the parameters, itwill continuously estimate based on the dynamic data input falling inthe data bins such that artificial data points will be overwritten inthe nearby bins, as discussed above, if the minimum cell voltage hitsthe trigger again. If the minimum cell voltage never achieves thetrigger again, the estimated minimum cell voltage will be graduallyclose to the average cell voltage since average cell voltage values willdominate the data input. In addition, once the parameter estimation iscomplete at the box 36, a desired current set-point value for the fuelcell stack 20 may be determined. A detailed discussion of determining acurrent set-point value for the fuel cell stack 20 can be found in U.S.patent application Ser. No. 12/567,381, entitled, “Method to ImproveFuel Cell System Performance Using Cell Voltage Prediction of Fuel CellStack,” assigned to the assignee of the present application andincorporated herein by reference.

The foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. One skilled in the art willreadily recognize from such discussion and from the accompanyingdrawings and claims that various changes, modifications and variationscan be made therein without departing from the spirit and scope of theinvention as defined in the following claims.

What is claimed is:
 1. A method for predicting a minimum cell voltagetrend of fuel cells in a fuel cell stack, said method comprising: usinga stack health monitor and a controller to perform the steps of:monitoring fuel cell stack voltage and current density of the fuel cellstack; determining an average cell voltage from the measured stackvoltage based on the number of fuel cells in the stack; monitoring aminimum cell voltage of the cells in the stack; determining whether theminimum cell voltage falls below a first predetermined threshold value;generating a first set of artificial data points for a cell voltageproximate to the first threshold value if the minimum cell voltage fallsbelow the first threshold value; determining whether the minimum cellvoltage falls below a second predetermined threshold value, wherein thesecond predetermined threshold value is less than the firstpredetermined threshold value; generating a second set of artificialdata points for a cell voltage proximate to the second threshold valueif the minimum cell voltage falls below the second threshold value; andestimating fuel cell stack parameters based on the average cell voltage,the current density, and the first and second set of artificial datapoints so as to predict the minimum cell voltage trend and a minimumcell voltage polarization curve of the fuel cells in the stack.
 2. Themethod according to claim 1 wherein generating the first set ofartificial data points includes generating the first set of artificialdata points within a predetermined current density range proximate tothe first predetermined threshold value for the minimum cell voltage. 3.The method according to claim 1 wherein generating the second set ofartificial data points includes generating the second set of artificialdata points within a predetermined current density range proximate tothe second predetermined threshold value for the minimum cell voltage.4. The method according to claim 1 wherein estimating the fuel cellstack parameters is performed continuously after the estimation beginsby using real-time average cell voltage and the corresponding currentdensity data of the fuel cell stack.
 5. The method according to claim 1further comprising using a power limitation algorithm to integrate theestimated fuel cell stack parameters to prevent fuel cell voltagepotential reversals from occurring.
 6. The method according to claim 1wherein monitoring minimum cell voltages, average cell voltages andcurrent densities includes using a stack health monitor.
 7. The methodaccording to claim 1 wherein monitoring average cell voltages andcurrent densities includes using a boost converter.
 8. A method forpredicting a minimum cell voltage trend of fuel cells in a fuel cellstack, said method comprising: using a stack health monitoring deviceand a controller to perform the steps of: measuring fuel cell stackvoltage and current density of the fuel cell stack; determining anaverage fuel cell voltage from the measured stack voltage based on thenumber of fuel cells in the stack; measuring a minimum cell voltage ofthe fuel cells in the stack; determining whether the minimum cellvoltage falls below a predetermined threshold value; generatingartificial data points for a fuel cell voltage proximate to thepredetermined threshold value if the minimum cell voltage falls belowthe predetermined threshold value; storing the average cell voltage, thecurrent density and the artificial data points; and predicting theminimum cell voltage trend and a minimum cell voltage polarization curvefrom the stored average cell voltage, the current density and theartificial data points.
 9. The method according to claim 8 whereingenerating the artificial data points includes generating the artificialdata points within a predetermined current density range proximate tothe predetermined threshold value for the average cell voltage.
 10. Themethod according to claim 8 further comprising estimating stackparameters using the stored average cell voltage, current density andthe artificial data points when the amount of information stored issufficient as determined by the current density range.
 11. The methodaccording to claim 10 wherein estimating the fuel cell stack parametersis performed continuously after the estimation begins by using real-timeaverage cell voltage and the corresponding current density data from thefuel cell stack.
 12. The method according to claim 11 wherein theestimated value of the minimum cell voltage from the real-time averagecell voltage and the corresponding current density will gradually becomecloser to the average cell voltage and the corresponding current densityif the fuel cell system does not achieve the predetermined thresholdvalue after initially achieving the predetermined threshold value. 13.The method according to claim 8 wherein measuring minimum cell voltage,stack voltage and current density includes utilizing a stack healthmonitor.
 14. The method according to claim 8 wherein measuring stackvoltage and current density includes utilizing a boost converter.
 15. Asystem for estimating parameters of a fuel cell stack, said systemcomprising: a stack health monitoring device for monitoring minimum cellvoltage of the fuel cells in the stack, stack voltage and currentdensity of the fuel cell stack, wherein the stack health monitorindicates when a predetermined minimum cell voltage threshold level hasbeen achieved; and a controller configured to control the fuel cellstack, said controller determining and recording the average fuel cellvoltage based on stack voltage and the number of fuel cells in thestack, said controller generating and storing artificial data pointsproximate to the one or more predetermined minimum cell voltagethreshold levels each time the minimum cell voltage drops below the oneor more predetermined minimum cell voltage threshold levels, asindicated by the stack health monitor, so as to provide an estimation ofthe fuel cell stack parameters including a minimum cell voltage trendand a minimum cell voltage polarization curve.
 16. The system accordingto claim 15 wherein the controller estimates the fuel cell stackparameters once the amount of data gathered is sufficient as determinedby the current density range.
 17. The system according to claim 16wherein the controller estimates the fuel cell stack parameterscontinuously after the estimation begins by using real-time average cellvoltage and the corresponding current density data from the fuel cellstack.
 18. The system according to claim 15 wherein the controllergenerates and stores the artificial data points within a predeterminedcurrent density range for each predetermined minimum voltage thresholdlevel.
 19. The system according to claim 15 wherein the controllerincludes a power limitation algorithm to prevent fuel cell voltagepotential reversals from occurring.
 20. The system according to claim 15further comprising a boost converter for measuring the average fuel cellvoltage and current density.